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Identification of differentially expressed genes (DEGs) is well recognized to be variable across independent replications of genome-wide transcriptional studies. These are often employed to characterize disease state early in the process of discovery and prioritize novel targets aimed at addressing unmet medical need. Increasing reproducibility of biological findings from these studies could potentially positively impact the success rate of new clinical interventions. This work demonstrates that statistically sound combination of gene expression data with prior knowledge about biology in the form of large protein interaction networks can yield quantitatively more reproducible observations from studies characterizing human disease. The novel concept of Well-Associated Proteins (WAPs) introduced herein—gene products significantly associated on protein interaction networks with the differences in transcript levels between control and disease—does not require choosing a differential expression threshold and can be computed efficiently enough to enable false discovery rate estimation via permutation. Reproducibility of WAPs is shown to be on average superior to that of DEGs under easily-quantifiable conditions suggesting that they can yield a significantly more robust description of disease. Enhanced reproducibility of WAPs versus DEGs is first demonstrated with four independent data sets focused on systemic sclerosis. This finding is then validated over thousands of pairs of data sets obtained by random partitions of large studies in several other diseases. Conditions that individual data sets must satisfy to yield robust WAP scores are examined. Reproducible identification of WAPs can potentially benefit drug target selection and precision medicine studies., Author summary Gene expression studies are commonly used to characterize biological systems. Genes identified in such experiments as expressed at different levels between conditions (e.g. healthy vs. disease) can indicate biological functions that are important in this context. However, it is well-recognized that such findings can vary substantially across independent investigations. We quantified reproducibility here under a conservative control scenario that partitions a given data set in two, independently of experimental conditions, for multiple data sets characterizing several diseases in humans. Furthermore, we have shown that it is possible to obtain more reproducible findings than DEGs, which we term Well-Associated Proteins, characterizing differences in gene expression between healthy and disease states. This was accomplished by combining gene expression and prior knowledge of functional relationships between gene products accumulated over many studies and publications. Resulting Well-Associated Proteins can be computed efficiently enough to enable permutation controls and demonstrate on average higher reproducibility than differentially expressed genes, both within and across data sets. This suggests that Well-Associated Proteins may better reflect differences in biology when comparing disease and healthy states than DEGs, thus representing an important step towards identification of key disease drivers.